airsketch: Real-Time Hand-drawn Recognition and Object Retrieval System

Abstract

airsketch is an innovative project aimed at bridging the gap between hand-drawn gestures in the air and real-world object identification. Leveraging camera modules for gesture detection, airsketch enables users to create drawings using their fingers in the air, which are then captured and plotted onto a digital canvas in real-time. The system employs advanced computer vision techniques to interpret the drawn shapes accurately.

Key Features:

1. Real-Time Gesture Recognition:

   • airsketch utilizes a camera module to detect hand-drawn gestures in the air.
   • The system provides instant feedback, plotting the drawn shapes onto a digital canvas with precision.

2. Object Retrieval from External API:

   • Upon completing a drawing, airsketch connects to an external API to fetch the closest matching object based on the user's hand-drawn creation.
   • The retrieved object is displayed to the user, enhancing their interactive and immersive experience.

3. Intelligent Object Matching:

   • The system employs machine learning algorithms to analyze the drawn gestures and identify the closest resembling objects from the external database.
   • Continuous learning ensures improved accuracy in object matching over time.

4. User-friendly Interface:

   • airsketch features an intuitive and user-friendly interface, allowing seamless interaction with the drawing canvas and object identification results.
   • Users can enjoy a dynamic and engaging experience as they explore the system's capabilities.

5. Extensibility and Integration:

   • The project is designed with extensibility in mind, enabling integration with various APIs for diverse object databases.
   • Developers can easily extend the system to support additional features or connect to different external services.

6. Educational and Entertainment Applications:

   • airsketch finds applications in both educational and entertainment contexts, providing a unique and interactive way for users to explore the world of object recognition through hand-drawn gestures.

airsketch redefines user interaction by combining the creativity of hand-drawn gestures with the power of object recognition. This project opens up possibilities for immersive educational experiences, interactive art creation, and novel ways of engaging with technology. With its real-time gesture recognition and intelligent object matching capabilities, airsketch presents a compelling solution for those seeking an innovative fusion of art and technology.

Existing System:

The Google AutoDraw experiment is an online application where users can draw doodles on a canvas, and the system uses machine learning to recognize the doodle and suggests relevant icons. The user interacts with the application solely through the canvas, and the suggestions are based on pre-trained models.

Disadvantages of Existing System:

1. Limited Features: The existing system lacks features like saving user information, storing drawings, and utilizing additional inputs such as webcam data for a more interactive experience.

2. No User Persistence: The system does not store user data or drawing history, making it difficult for users to track their previous interactions.

Proposed System:

The enhanced system aims to improve the AutoDraw experiment by incorporating the following features:

1. Name Fetching: Instead of user accounts, the system will fetch the user's name at the beginning of the session to personalize the experience.

2. Drawing and Inference Logging: The system will save both the drawings made by the user and the corresponding model inferences, creating a personal database of user interactions.

3. Webcam Integration: Users can use their webcams to detect finger movements, and this input will be translated into doodles on the canvas. The model will then analyze these doodles to suggest icons.

Advantages of Proposed System Over Existing:

1. Enhanced User Experience: With features like name fetching, drawing history, and webcam integration, the proposed system provides a more personalized and interactive experience.

2. Improved Data Persistence: The system's ability to save user information and drawing history enhances data persistence, allowing users to pick up where they left off and explore their creative journey.

Algorithms Used:

1. Machine Learning Model: Utilize a machine learning model for recognizing doodles and suggesting icons. Google's Quick, Draw! dataset can be used for training and fine-tuning the model.

2. Hand Gesture Recognition: Implement a hand gesture recognition algorithm to interpret webcam data and convert finger movements into doodles on the canvas.

Rough Product Backlog:

1. Name Fetching and Database Setup:

   • Fetch the user's name at the beginning of the session.
   • Design a database schema for storing user information, drawings, and inferences.

2. Drawing and Inference Logging:

   • Implement logging mechanisms to record user drawings and model inferences.

3. Webcam Integration:

   • Develop a webcam integration module for capturing hand gestures.
   • Integrate hand gesture data with the canvas for real-time doodling.

4. UI/UX Enhancements:

   • Design an intuitive and user-friendly interface for both desktop and mobile users.
   • Incorporate features like tooltips, guidance, and feedback to enhance user experience.

5. Model Integration and Training:

   • Integrate the machine learning model for doodle recognition.
   • Train and fine-tune the model using relevant datasets.

6. Testing and Optimization:

   • Conduct thorough testing of the entire system, focusing on usability, security, and performance.
   • Optimize the system for responsiveness and efficiency.